In some applications, sensors or cameras positioned on unmanned aerial vehicles (UAVs) or other airborne platforms may be used to track objects moving on the ground. Such systems may be used in civil applications, such as tracking vehicles by law enforcement and other governmental authorities, or in military applications, such as tracking mobile missiles or armored vehicles by military and intelligence organizations. Examples of prior art UAV-based systems include those systems disclosed, for example, in U.S. Pat. No. 6,712,312 B1 issued to Kucik, U.S. Pat. No. 5,575,438 issued to McGonigle et al., and U.S. Pat. No. 3,778,007 issued to Kearney et al.
In general, as an object moves along the ground, a tracking sensor on the UAV or other airborne platform (e.g. missile) may be steered to prevent the moving object from disappearing from the sensor's field of view. Updates on the position (and velocity) of the moving object may be transmitted to the UAV by a third party observer (or support system) to enable an on-board system to issue pointing commands to steer the tracking sensor to continue tracking the moving object. These updates are generally referred to as “data link updates.” Examples of third party observers include an Intelligent Surveillance and Reconnaissance (ISR) platform and a ground observer with a portable target designation system.
The data link updates of existing third party observer systems usually have large measurement uncertainties. Unless special arrangements are made to improve the accuracy of these data link updates, direct use of existing data link data without any filtering may cause the on-board sensor to abruptly transition from one look angle to another when a data link update occurs. Since a sensor tracker is designed to track a moving object within a relatively stable scene, the sensor may lose track of a moving object when the scene changes abruptly.
Some on-board systems feed the data link updates to a Kalman filter to estimate and predict the path the moving objects are taking, and thus provide a smoother pointing command. A Kalman filter is typically characterized by statistical models of data link updates and process noise. The Kalman filtering process involves an iterative algorithm that requires several data link updates for its output to converge if the selected uncertainty models match the true statistics. If the statistical information about the data link update error and process noise model does not match the true statistics, the Kalman filter will either oscillate or diverge, resulting in the on-board system being unable to properly steer the sensor to track the moving object. Existing methods of filtering (e.g. adaptive Kalman filtering, or mode switching Kalman filtering) undesirably require a relatively large number of data link updates for the iterative process to converge and thus provide smooth sensor pointing commands to adequately prevent oscillation or divergence of the on-board tracking system. Therefore, novel systems and methods which provide stable sensor pointing commands using fewer, more sporadic data link updates, and using data link updates having relatively large statistical variability, such as those generated by a ground operator with a portable target designation system and existing legacy surveillance systems, would have utility.